Evaluation of the Psychometric Properties of the Malaysian Version of Ottawa Decisional Conflict Scale among Cardiovascular Patients Undergoing Major Surgery
Bibliographic record
Abstract
Background: The Ottawa Decisional Conflict Scale (ODCS) is one of the initiatives developed to determine the information about the patient's decision and the factor that influence the decision made. Therefore, a systematic and structured process of decision-making can express the difficult action to be taken by patients. Objectives: This study aimed to evaluate the psychometric properties of the Malaysian version of ODCS among cardiovascular patients undergoing major surgery. Methods: This study used the forward-backward translation method to develop an instrument that enabled Malaysians to know about this variable. Therefore, the researcher decided to make a transcultural adaptation and evaluate the decision-making of the Malaysia version of the ODCS, which seeks information about decision-making and the factors that influence the choices made. This study was conducted from January 2015 to July 2016 through a convincing sampling of 520 cardiovascular patients who need to undergo major surgery with a focus on decision-making regarding the diseases. Results: The results obtained on the reliability tests showed good internal consistency for all items (Cronbach α=0.914-0.917). From the analysis, the Kaiser-Meyer-Olkin Measure of Sampling Adequacy was 0.886, while the significant value of Bartlett's Test of Sphericity was P<0.001. Therefore, the analysis concluded that the data were appropriate for principal component analysis. Conclusion: The psychometric properties of the Malaysian version of the ODCS are considered appropriate to be administered to patients who need to undergo cardiac surgery. Patients' provision of information was able to decrease decisional conflict among them with cardiovascular disease.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.006 | 0.023 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 0.000 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".